The current work showed the general applicability of Monte Carlo method based variation estimation techniques on the variational behavior of rolling processes.
Two different kinds of variation sources in rolling processes were identified: first, variations in the input workpiece, second, variations within the process.
They showed fundamentally different behavior.
While variations in the input workpiece tend to vanish along the process, variations in the process itself accumulate and lead to high variations in the product.
This leads to the conclusion, that the control of variations within the process is crucial in comparison to variations in the input material.
Especially in the case of manual feeding of a reversing mill, as common in pilot plants, this has to be kept in mind.
It can be stated, that a well controlled process should tend to decrease the variations of its input workpiece.
These findings are in accordance to practical experience.

In following work, the concept will be extended by the use of more advanced models within the simulation core.
However, this has to be done always with the computational effort in mind, since the core simulation must be carried out up to hundreds of times.
With the use of locally resolved models, estimations regarding the variation of local workpiece state can be carried out.
Especially in terms of microstructure evolution, this would be a valuable contribution to the interpretation of experimental results.
In the future, this approach can help to identify sources of variations within a process and evaluate their influence on the resulting output variations.